Consistency and identifiability of football teams: a network science perspective

Abstract

We investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams’ matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match.

What Is the Relevance in the Passing Action between the Passer and the Receiver in Soccer? Study of Elite Soccer in La Liga

“…The main aim of this study was to analyzes passes with regard to the trajectory made by the receiver of the ball and the space where the player received it in relation to the closest direct defender and his efficiency in finishing the play. Previous related literature has not explored these aspects; we only found work on the importance of gaining space when receiving the ball [ 19 ] and pass networks [ 32 , 33 , 34 , 35 ]. Thus, important information was provided by this study about where and how the ball receiver should move.…”

“…Other authors studied team tactics using pass networks; for example, they looked at the players who were most important within the team and the connections that were made among the players through passing [ 32 , 33 , 34 ]. Garrido et al [ 35 ] used pass networks to calculate consistency and identifiability rankings over an entire season, for which Real Madrid CF was detected as having the highest consistency in their pass networks and FC Barcelona obtained the highest identifiability. Voronoi’s diagrams have also been used to analyze space dominance and overtaking of players in passing, resulting in a higher average for match wins when there were fewer of the opposing team’s players between the ball and the opponent’s goal and thus a larger area of the pitch [ 19 ].…”

Stochastic model for football's collective dynamics

“…The structure of interactions that turns out from this approach can be considered a new metric for this sport. In this sense, our analysis complement recent contributions in the framework of network science [27][28][29][30][31][32]. In addition, many of the metrics of current use in this sport can be tested through our model [17][18][19][20][21][22].…”

“…Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.…”

“…Furthermore, temporal sequences of ball and player movements in football, showing traits of complex behaviors, have been reported and studied using stochastic models and statistical analysis [23][24][25][26].Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.…”